The amount of information available in the World Wide Web and other document corpora continues to expand at a significant pace. Users with a connection to the Internet or other networks are able to search for and identify a large volume of content that would previously have very been difficult or even impossible to find. Unfortunately, the ease of locating certain content varies significantly by the format of the content that is sought. While users seeking textual content on the World Wide Web have a number of good search services such as google.com to identify results responsive to a search query, the performance of tools to search for other types of content have lagged far behind. In particular, many types of media, including but not limited to, audio, video and images, are difficult to classify and locate for retrieval in an automated fashion (an instance of any type of media will hereinafter be referred to as a media content unit).
Media content units are particularly challenging to accurately search because there are few reliable algorithms that can programmatically analyze the content in a media content unit and store the media content unit in a manner that allows it to be efficiently located. For example, a human user looking at a picture of a sailboat will very quickly be able to determine not only that the image is a sailboat, but also extract other pieces of information about the image such as the number of people on the sailboat, the color of the sailboat sails, and the condition of the surrounding seas. While image evaluation systems have been developed that use various techniques to describe and classify images, detecting the colors, shapes, and textures of an image to derive the subject matter of the image is an incredibly challenging and complex problem. As a result, automated image evaluation systems have fallen short of the performance necessary to become commercially successful. The same limitations and problems extend to other media content, such as audio or video, as well.
Because of the performance limitations of automated media content recognition systems, some systems have relied upon a file name or on metadata associated with a media content unit to predict the contents of the media content unit. Unfortunately, however, using the file name or metadata provided by a large body of users may not provide optimum results since different users often use vastly different naming conventions to describe the same content. For example, an image of a sailboat under sail may be characterized by one user as “hard tack to port” whereas another user may characterize it as “enjoying a gusty day on the seas.” Search engines that rely upon vocabulary selected by a large body of users to characterize media may therefore return inherently unreliable results.
A different approach is taken by U.S. Pat. No. 6,735,583 to Bjarnestam et al. entitled “Method and System for Classifying and Locating Media Content,” and assigned to the applicants of the present case. Bjarnestam et al. disclose a structured vocabulary system that may be used in a media classification and search system to provide a better index to media content. Such a system solves many of the problems identified above, but does so with a manually intensive process. While the system disclosed in Bjarnestam et al. provides superior performance to other existing technologies, it would be beneficial to further automate the classification of media content units in order to make available for searching a greater number of media content units.